Random Forest Algorithm-Based Ultrasonic Image in the Diagnosis of Patients with Dry Eye Syndrome and Its Relationship with Tear Osmotic Pressure
The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and th...
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| Vydáno v: | Computational and mathematical methods in medicine Ročník 2022; s. 1 - 8 |
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28.02.2022
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| Abstract | The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8±30.6) μm and (29.1±30.9) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was (−6.31±2.82) μm, and that in group B was (−6.45±3.06) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was (−3.78±1.13) μm in group A and (−7.09±2.05) μm in group B (P<0.05). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P<0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome. |
|---|---|
| AbstractList | The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8 ± 30.6) μm and (29.1 ± 30.9) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was (-6.31 ± 2.82) μm, and that in group B was (-6.45 ± 3.06) μm. The 95% confidence interval of the difference between the two is -7.66~-5.43 μm. In patients with severe dry eye, the average CCT was (-3.78 ± 1.13) μm in group A and (-7.09 ± 2.05) μm in group B (P < 0.05). The 95% confidence interval of the difference between the two is -7.05~ -5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P < 0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome.The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8 ± 30.6) μm and (29.1 ± 30.9) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was (-6.31 ± 2.82) μm, and that in group B was (-6.45 ± 3.06) μm. The 95% confidence interval of the difference between the two is -7.66~-5.43 μm. In patients with severe dry eye, the average CCT was (-3.78 ± 1.13) μm in group A and (-7.09 ± 2.05) μm in group B (P < 0.05). The 95% confidence interval of the difference between the two is -7.05~ -5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P < 0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome. The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8 ± 30.6) m and (29.1 ± 30.9) m, respectively. 95% confidence interval was 22.7-34.2 m. In patients with moderate dry eye, the average CCT measured in group A was (-6.31 ± 2.82) m, and that in group B was (-6.45 ± 3.06) m. The 95% confidence interval of the difference between the two is -7.66~-5.43 m. In patients with severe dry eye, the average CCT was (-3.78 ± 1.13) m in group A and (-7.09 ± 2.05) m in group B ( < 0.05). The 95% confidence interval of the difference between the two is -7.05~ -5.11 m. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe ( < 0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome. The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were ( 27.8 ± 30.6 ) μm and ( 29.1 ± 30.9 ) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was ( − 6.31 ± 2.82 ) μm, and that in group B was ( − 6.45 ± 3.06 ) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was ( − 3.78 ± 1.13 ) μm in group A and ( − 7.09 ± 2.05 ) μm in group B ( P < 0.05 ). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe ( P < 0.05 ). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome. The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8±30.6) μm and (29.1±30.9) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was (−6.31±2.82) μm, and that in group B was (−6.45±3.06) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was (−3.78±1.13) μm in group A and (−7.09±2.05) μm in group B (P<0.05). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P<0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome. The study was to investigate the diagnostic value of ultrasound based on the random forest segmentation algorithm for dry eye disease and the relationship between dry eye degree and tear osmotic pressure. Specifically, 100 patients with dry eye syndrome were selected as the research subjects, and they were divided into group A (conventional ultrasonic detection) and group B (ultrasonic detection based on the random forest segmentation algorithm), with 50 patients in each group. An ultrasonic measurement was used as the gold standard to evaluate the effect of ultrasonic diagnosis. The degree of dry eye was determined by Ocular Surface Disease Index (OSDI) Questionnaire and DR-1 tear film lipid layer (TFLL) test. The tear osmotic pressure was measured, and the relationship between the degree of dry eye disease and the tear osmotic pressure was analyzed. The results showed that the ultrasonic imaging effect and each index based on random forest algorithm were better than the traditional graph cut algorithm. The average central corneal thickness (CCT) values of group A and group B were (27.8 ± 30.6) μm and (29.1 ± 30.9) μm, respectively. 95% confidence interval was 22.7-34.2 μm. In patients with moderate dry eye, the average CCT measured in group A was (−6.31 ± 2.82) μm, and that in group B was (−6.45 ± 3.06) μm. The 95% confidence interval of the difference between the two is −7.66~−5.43 μm. In patients with severe dry eye, the average CCT was (−3.78 ± 1.13) μm in group A and (−7.09 ± 2.05) μm in group B (P < 0.05). The 95% confidence interval of the difference between the two is −7.05~ −5.11 μm. In spearman correlation analysis, tear osmotic pressure increased with dry eye severity. There was a statistically significant difference between the moderate and the severe (P < 0.05). Tear osmotic pressure can be a rapid diagnostic index of dry eye severity. Ultrasound based on the random forest segmentation algorithm has high clinical application value in the diagnosis of dry eye syndrome. |
| Author | Sun, Zhuo Jiang, Lei Sun, Shanshan Chen, Juan |
| AuthorAffiliation | Department of Ophthalmology, The Third Peoples' Hospital of Changzhou, Changzhou, 213001 Jiangsu, China |
| AuthorAffiliation_xml | – name: Department of Ophthalmology, The Third Peoples' Hospital of Changzhou, Changzhou, 213001 Jiangsu, China |
| Author_xml | – sequence: 1 givenname: Lei orcidid: 0000-0002-7492-3663 surname: Jiang fullname: Jiang, Lei organization: Department of OphthalmologyThe Third Peoples’ Hospital of ChangzhouChangzhou213001 JiangsuChina – sequence: 2 givenname: Shanshan orcidid: 0000-0002-7823-0012 surname: Sun fullname: Sun, Shanshan organization: Department of OphthalmologyThe Third Peoples’ Hospital of ChangzhouChangzhou213001 JiangsuChina – sequence: 3 givenname: Juan orcidid: 0000-0002-8229-5177 surname: Chen fullname: Chen, Juan organization: Department of OphthalmologyThe Third Peoples’ Hospital of ChangzhouChangzhou213001 JiangsuChina – sequence: 4 givenname: Zhuo orcidid: 0000-0001-5340-1594 surname: Sun fullname: Sun, Zhuo organization: Department of OphthalmologyThe Third Peoples’ Hospital of ChangzhouChangzhou213001 JiangsuChina |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35265174$$D View this record in MEDLINE/PubMed |
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| Cites_doi | 10.3390/ijms21238966 10.1177/1120672120907315 10.3390/jcm10173860 10.1016/j.jtos.2019.05.007 10.1177/1352458520921364 10.1016/j.jfo.2018.11.001 10.1038/s41598-020-62583-x 10.17849/insm-47-01-31-39.1 10.4103/ojo.OJO_96_2018 10.3390/e22101129 10.1016/j.preteyeres.2018.02.001 10.1016/j.ajoc.2020.101003 10.1097/ICO.0000000000002643 10.1167/iovs.17-23538 10.1542/peds.2019-1402 10.1186/s12886-020-01404-1 10.1111/aos.13526 10.1007/s10792-017-0699-8 10.1097/NHH.0000000000000652 10.1016/j.ijcce.2020.12.004 10.1167/iovs.17-23475 10.22336/rjo.2019.4 |
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| SubjectTerms | Adult Algorithms Computational Biology Computer Simulation Corneal Pachymetry Decision Trees Dry Eye Syndromes - diagnostic imaging Dry Eye Syndromes - metabolism Female Humans Male Middle Aged Osmotic Pressure Severity of Illness Index Tears - chemistry Ultrasonography - statistics & numerical data |
| Title | Random Forest Algorithm-Based Ultrasonic Image in the Diagnosis of Patients with Dry Eye Syndrome and Its Relationship with Tear Osmotic Pressure |
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